Tempering–preservation treatment inactivated lipase in wheat bran and retained phenolic compounds

Wheat bran is rich in functional ingredients, but the high level of lipase limits its applications. Tempering–preservation treatment (at 70–90 °C with moisture of 20%–40% for 1–4 h) was exploited for stabilising wheat bran and its effect on polyphenols was investigated. The results showed that more lipase was inactivated at higher tempering moisture, temperature and longer time. The optimum condition for inactivation of wheat bran lipase was 30% moisture and 90 °C for 4 h. The inactivation rate reached 93.8% with a residual enzyme activity of 0.264 U g⁻¹. Under the optimum condition, the sum of free phenolic acids rose from 25.4 to 55.8 µg g⁻¹. As for bound phenolic acids, there was a slight increase of hydroxybenzoic acid derivatives but a slight decrease of hydroxycinnamic acid derivatives. The total contents of phenolic acids before and after stabilisation were not significantly different. This study showed the possibility of using tempering–preservation as an efficient method for inactivation of wheat bran lipase while maintaining its phenolic compounds, which could be used in the production of whole wheat flour.     

天然气管道放散计算与速查图

结合工程需要和实际情况，应用实际气体状态方程得到放散量计算式。放散时间计算式选取临界流计算模型。将两个计算式中的固定值和影响因素单独归纳，分别绘制出天然气管道放散量计算系数和放散时间计算系数速查图，给出计算流程。经实例验证，计算值与实际值相对误差不超过5%,此计算方法的实用性较强，能很好地满足工程上对放散量计算方便性和准确性的要求，对指导施工具有一定实用意义。文末附有放散量计算系数和放散时间计算系数速查图下载链接，也可扫二维码下载。     

Integrated Inspection of QoM,QoP, and QoS for AOI Industries in Metaverses

With the rapid development of information technologies such as digital twin, extended reality, and blockchain, the hype around "metaverse" is increasing at astronomical speed. However, much attention has been paid to its entertainment and social functions. Considering the openness and interoperability of metaverses, the market of quality inspection promises explosive growth. In this paper, taking advantage of metaverses, we first propose the concept of Automated Quality Inspection (AutoQI), which performs integrated inspection covering the entire manufacturing process, including Quality of Materials, Quality of Manufacturing (QoM), Quality of Products, Quality of Processes (QoP), Quality of Systems, and Quality of Services (QoS). Based on the scenarios engineering theory, we discuss how to perform interactions between metaverses and the physical world for virtual design instruction and physical validation feedback. Then we introduce a bottom-up inspection device development workflow with productivity tools offered by metaverses, making development more effective and efficient than ever. As the core of quality inspection, we propose Quality Transformers to complete detection task, while federated learning is integrated to regulate data sharing. In summary, we point out the development directions of quality inspection under metaverse tide.      

基于多通道的ICU脑血管疾病死亡风险预测模型

死亡风险预测指根据病人临床体征监测数据来预测未来一段时间的死亡风险。对于ICU病患，通过死亡风险预测可以有针对性地对病人做出临床诊断，以及合理安排有限的医疗资源。基于临床使用的MEWS和Glasgow昏迷评分量表，针对ICU病人临床监测的17项生理参数，提出一种基于多通道的ICU脑血管疾病死亡风险预测模型。引入多通道概念应用于BiLSTM模型，用于突出每个生理参数对死亡风险预测的作用。采用Attention机制用于提高模型预测精度。实验数据来自MIMIC [Ⅲ]数据库，从中提取3?080位脑血管疾病患者的16?260条记录用于此次研究，除了六组超参数实验之外，将所提模型与LSTM、Multichannel-BiLSTM、逻辑回归（logistic regression）和支持向量机（support vector machine, SVM）四种模型进行了对比分析，准确率Accuracy、灵敏度Sensitive、特异性Specificity、AUC-ROC和AUC-PRC作为评价指标，实验结果表明，所提模型性能优于其他模型，AUC值达到94.3%。